Fractal butterflies of Dirac fermions in monolayer and bilayer graphene
Bibliographic record
Abstract
Bloch electrons in a perpendicular magnetic field exhibit unusual dynamics that has been studied for more than half a century. The single‐electron energy spectrum of this system, the Hofstadter butterfly has been the subject of theoretical and experimental investigations for the past two decades. Experimental observation of these unusual spectra in semiconductor nanostructures, however, met with only limited success. The fractal nature of the butterfly spectrum was finally observed in 2013, thanks to the unique electronic properties of graphene. Here, the authors present an overview of the theoretical understanding of Hofstadter butterflies in monolayer and bilayer graphene. First, they briefly discuss the energy spectra in conventional semiconductor systems. The electronic properties of monolayer and bilayer graphene are then presented. Theoretical background on the Moiré pattern in graphene and its application in the magnetoconductance probe that resulted in graphene butterflies are explained. They have also touched upon the important role of electron–electron interaction in the butterfly pattern in graphene. Experimental efforts to investigate this aspect of fractal butterflies have just begun. They conclude by discussing the future prospects of butterfly search, especially for interacting Dirac fermions in graphene.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".